Field of the Invention
The disclosed embodiments relate in general to the field of social networking technology and in particular to computer-implemented systems and methods for detecting objectionable content, such as pornographic videos in a social networking context.
Description of the Related Art
Various methods exist in the art for detecting pornographic videos and images. The most obvious method is to have human moderators manually review, classify and, if appropriate, block all content uploaded by the users. However, for large social networks, where users upload thousands or even millions of content files daily, this method is highly impractical.
On the other hand, most of automated methods for content classification rely on image or video frame analysis and have varying reliability. For example, one such method is based on detecting skin color tones within the center region of the images, as pornographic images and videos tend to have a lot of skin in the picture, see Jiann-Shu Lee et al., Naked image detection based on adaptive and extensible skin color model, Pattern Recognition 40 (2007) 2261-2270. However, while this algorithm achieves 80% effectiveness of port detection, it is also prone to generating a very high false alarm rates.
Another algorithm relies on human users to flag pornographic content, which is subsequently manually reviewed by moderators, who make the final content classification decisions. However, this method also performs relatively poorly as flagging the inappropriate content requires an extra effort from the user, which negatively impacts user participation.
Yet another method relies on content file metadata analysis, which looks for pornography-related key phrases in the file names and other available textual components associated with the content. Needless to say that this method is also error-prone as the content metadata is not always indicative of the type of content itself. In addition, widely available metadata scrubbers make pornography detection using this method an even more challenging task.
Therefore, new and improved systems and methods for detecting pornographic materials in social networks are needed that are not subject to above and other deficiencies of the conventional technology.